Masker – PDF Redaction

Hacker News

I built Masker after needing to share financial PDFs with a tax strategist without also sharing names, addresses, tax IDs, phone numbers, and account numbers. It is a native macOS app. It finds repeated PII, uses Apple’s on-device OCR for scanned pages, and lets you review every mask before export. Institution names and amounts can remain visible while account identifiers are covered. Mask sets and labels can be reused across folders. Everything runs locally. The tests generate fake PDFs, export them, and use text extraction and OCR to check that the selected values are gone. Built with Swift, SwiftUI, PDFKit, and Codex. Free and open source. Feedback on missed matches, false positives, and the folder workflow would be useful.

Visit product View on Hacker NewsAug 26, 2026

Share card

Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

79%79% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: mac, macos, apple · Missing: agents, agent, cursor

76%76% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: open source, ide, io · Missing: https docs, excited, just released

48%48% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

29%29% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

Similar products

A Semiring of Functions [pdf]51%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

A Semiring of Functions [pdf]

Hacker News9

An introduction to 'Conscious Turing Machines' with fizz [pdf]62%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

An introduction to 'Conscious Turing Machines' with fizz [pdf]

Hacker News2

Pdf.to50%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Pdf.to

Hacker News42

PDF Annotator of Nightmares50%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

PDF Annotator of Nightmares

Hacker News25

PDF Differ50%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

PDF Differ

Hacker News62

PDF Crawler63%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

PDF Crawler

Hacker News3

SafeConverter7%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

PDF sem limites, sem cadastro e sem dor de cabeça

Product Hunt

PDF Redaction36%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

AI-powered PDF Redaction

Indie Hackerscommitment-side-project

Rat PDF48%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Your all-in-one PDF toolkit

Indie Hackers1$10,000/moprivacy-security

Pdf.to – Convert PDF's to and From53%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Pdf.to – Convert PDF's to and From

Hacker News1

Browse all products Analyze your launch